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Reducing Student Hint Use by Creating Buggy Messages from Machine Learned Incorrect Processes.

, and . Intelligent Tutoring Systems, volume 8474 of Lecture Notes in Computer Science, page 674-675. Springer, (2014)

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The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment., , , , , and . LAK, page 279-288. ACM, (2016)Reducing Student Hint Use by Creating Buggy Messages from Machine Learned Incorrect Processes., and . Intelligent Tutoring Systems, volume 8474 of Lecture Notes in Computer Science, page 674-675. Springer, (2014)ASSISTments Dataset from Multiple Randomized Controlled Experiments., , and . L@S, page 181-184. ACM, (2016)Applying and Exploring Bayesian Hypothesis Testing for Large Scale Experimentation in Online Tutoring Systems., and . L@S, page 229-232. ACM, (2017)Crowdsourcing and Education: Towards a Theory and Praxis of Learnersourcing., , , , , , , , and . ICLS, International Society of the Learning Sciences, (2018)Using a Single Model Trained across Multiple Experiments to Improve the Detection of Treatment Effects., , , , and . EDM, International Educational Data Mining Society (IEDMS), (2017)Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps., , , , , and . EDM, page 413-414. International Educational Data Mining Society (IEDMS), (2014)When More Intelligent Tutoring in the Form of Buggy Messages Does not Help., and . AIED, volume 9112 of Lecture Notes in Computer Science, page 768-771. Springer, (2015)